Bridging the Gap in Hybrid Decision-Making Systems
This work addresses the challenge of integrating human and machine decision-making in labeling tasks, though it appears incremental as it builds on existing hybrid paradigms.
The authors tackled the problem of hybrid decision-making by introducing BRIDGET, a human-in-the-loop system that dynamically switches between human-led and machine-led paradigms to label unlabeled datasets, aiming to bridge the gap between these approaches.
We introduce BRIDGET, a novel human-in-the-loop system for hybrid decision-making, aiding the user to label records from an un-labeled dataset, attempting to ``bridge the gap'' between the two most popular Hybrid Decision-Making paradigms: those featuring the human in a leading position, and the other with a machine making most of the decisions. BRIDGET understands when either a machine or a human user should be in charge, dynamically switching between two statuses. In the different statuses, BRIDGET still fosters the human-AI interaction, either having a machine learning model assuming skeptical stances towards the user and offering them suggestions, or towards itself and calling the user back. We believe our proposal lays the groundwork for future synergistic systems involving a human and a machine decision-makers.